Educating the Elite: A Social Justice Education for the Privileged Class
Bibliographic record
Abstract
America is witnessing a new gilded age. Since the 1970’s, inequality in wealth and income has soared within the United States—and globally (Sayer, 2016; Therborn, 2013). Such inequalities affect human flourishing because they allow the privileged class to convert their wealth into different, and unequal, lifestyles and life chances. In addition, such inequalities provide the privileged class with greater opportunity to convert their wealth, income, and social capital into influence within the political system that undermines democracy. Considering the vast class-based inequities, then, how can social justice educators help the students born into the world of class privilege understand their civic obligations to deepen democracy—particularly economic democracy? And, how can they do so without engaging in morally reprehensible teaching practices? This paper takes a ‘critical approach’ in attempting answer this question. First by analyzing the cultural and structural causes of behind the world of class privilege—what I term the pathology of privilege. As well as, explaining how the pathology of privileges undermines democracy. Then I analyze four possible social justice approaches for the class privilege—class suicide; political apathy; civic volunteerism; and activist ally. I concluded by explaining why the activist ally approach is both a more crucial and morally appropriate approach for educating the elite about their responsibility to deepen democracy and advance justice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".